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Related Concept Videos

Precipitation Titration Curve: Analysis01:21

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The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...
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A type of Lewis acid-base chemistry involves the formation of a complex ion (or a coordination complex) comprising a central atom, typically a transition metal cation, surrounded by ions or molecules called ligands. These ligands can be neutral molecules like H2O or NH3, or ions such as CN− or OH−. Often, the ligands act as Lewis bases, donating a pair of electrons to the central atom. These types of Lewis acid-base reactions are examples of a broad subdiscipline called coordination...
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Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
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Related Experiment Video

Updated: Nov 14, 2025

Salinity-dependent Toxicity Assay of Silver Nanocolloids Using Medaka Eggs
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Setting a Protective Threshold Value for Silver Toward Freshwater Organisms.

Katrien Arijs1,2, Charlotte Nys2,3, Patrick Van Sprang2

  • 1European Precious Metal Federation, Brussels, Belgium.

Environmental Toxicology and Chemistry
|March 11, 2021
PubMed
Summary

New research provides a reliable threshold for ionic silver (Ag) in freshwater, crucial for environmental protection. A new ecotoxicity assessment established a safe level of 0.116 µg dissolved Ag/L for 95% of species.

Keywords:
Freshwater toxicologyMetal toxicitySilverSpecies sensitivity distributionsWater quality criteria

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Area of Science:

  • Environmental Toxicology
  • Ecotoxicology
  • Risk Assessment

Background:

  • European regulations (EC) No. 1272/2008 and the Water Framework Directive 2000/60/EC mandate re-evaluation of ecotoxicity data for substances like ionic silver (Ag).
  • Existing chronic freshwater ecotoxicity data for Ag required rigorous quality assessment.

Purpose of the Study:

  • To re-evaluate existing chronic freshwater ecotoxicity data for ionic silver (Ag) based on strict quality criteria.
  • To generate new chronic ecotoxicity data for sensitive aquatic species, including rotifers, cyanobacteria, and aquatic plants.
  • To establish a protective threshold for Ag in freshwater ecosystems.

Main Methods:

  • A comprehensive review and quality assessment of available chronic freshwater ecotoxicity data for Ag.
  • Generation of new ecotoxicity data using standardized testing protocols for *Brachionus calyciflorus*, *Anabaena flos-aquae*, and *Lemna minor* exposed to silver nitrate.
  • Application of species sensitivity distribution (SSD) models to a curated dataset of 15 species across 12 taxonomic groups.

Main Results:

  • New chronic ecotoxicity data yielded 10% effect concentrations (EC10) of 0.31 µg/L for *B. calyciflorus*, 0.41 µg/L for *A. flos-aquae*, and 1.40 µg/L for *L. minor*.
  • The best-fitting SSD model (Rayleigh distribution) identified a threshold value of 0.116 µg dissolved Ag/L protective for 95% of freshwater species.
  • This threshold value accounts for reasonable worst-case bioavailability conditions in European Union surface waters (low hardness, low dissolved organic carbon).

Conclusions:

  • The derived threshold of 0.116 µg dissolved Ag/L is a reliable and conservative value for protecting freshwater species.
  • This threshold provides a solid scientific basis for setting environmental quality standards for Ag in freshwater.
  • The study emphasizes the importance of robust data quality and advanced modeling for effective environmental risk assessment.